Useful R functions (detailed)

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 214 493 428 994 685 431 134  61 351 986 285 976 181 884 772 999  91 495 893 319

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 134 214 976 772 181 319 986  91 493 428  NA 495 285 351  NA 994 685  NA  61 884 431 893 999

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 5 2 5 1 5 2 4 5 4 2

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "t" "e" "p" "a" "g" "P" "S" "G" "K" "J"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1]  4 10 12 16

which( manyNumbersWithNA > 900 )

[1]  3  7 16 23

which( is.na( manyNumbersWithNA ) )

[1] 11 15 18

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 994 986 976 999

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 994 986 976 999

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 994 986 976 999

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "P" "S" "G" "K" "J"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "t" "e" "p" "a" "g"

manyNumbers %in% 300:600

 [1] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  2  3  6  9 18 20

sum( manyNumbers %in% 300:600 )

[1] 6

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "small" "small" "large" "large" "small" "small" "large" "small" "small" "small" NA      "small" "small" "small" NA     
[16] "large" "large" NA      "small" "large" "small" "large" "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "small"   "small"   "large"   "large"   "small"   "small"   "large"   "small"   "small"   "small"   "UNKNOWN" "small"  
[13] "small"   "small"   "UNKNOWN" "large"   "large"   "UNKNOWN" "small"   "large"   "small"   "large"   "large"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1]   0   0 976 772   0   0 986   0   0   0  NA   0   0   0  NA 994 685  NA   0 884   0 893 999

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 5 2 1 4

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  5  2  1  4

duplicated( duplicatedNumbers )

 [1] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 23

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 999

which.min( manyNumbersWithNA )

[1] 19

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 61

range( manyNumbersWithNA, na.rm = TRUE )

[1]  61 999

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 134 214 976 772 181 319 986  91 493 428  NA 495 285 351  NA 994 685  NA  61 884 431 893 999

sort( manyNumbersWithNA )

 [1]  61  91 134 181 214 285 319 351 428 431 493 495 685 772 884 893 976 986 994 999

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  61  91 134 181 214 285 319 351 428 431 493 495 685 772 884 893 976 986 994 999  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 999 994 986 976 893 884 772 685 495 493 431 428 351 319 285 214 181 134  91  61  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 134 214 976 772 181

order( manyNumbersWithNA[1:5] )

[1] 1 5 2 4 3

rank( manyNumbersWithNA[1:5] )

[1] 1 3 5 4 2

sort( mixedLetters )

 [1] "a" "e" "g" "G" "J" "K" "p" "P" "S" "t"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 9.0 6.5 6.5 4.5 9.0 2.5 9.0 1.0 4.5 2.5

rank( manyDuplicates, ties.method = "min" )

 [1] 8 6 6 4 8 2 8 1 4 2

rank( manyDuplicates, ties.method = "random" )

 [1]  8  7  6  5 10  3  9  1  4  2

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000 -1.43295010 -2.27817614  0.08998173  1.79937099  1.52399766
[11] -0.16566794  0.54867070 -1.15039510  0.52715497  0.94365527

round( v, 0 )

 [1] -1  0  0  0  1 -1 -2  0  2  2  0  1 -1  1  1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -1.4 -2.3  0.1  1.8  1.5 -0.2  0.5 -1.2  0.5  0.9

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -1.43 -2.28  0.09  1.80  1.52 -0.17  0.55 -1.15  0.53  0.94

floor( v )

 [1] -1 -1  0  0  1 -2 -3  0  1  1 -1  0 -2  0  0

ceiling( v )

 [1] -1  0  0  1  1 -1 -2  1  2  2  0  1 -1  1  1

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 × 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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